Speciation relations-inspired adaptive node aggregation and data reasoning in Sparse Mobile Crowdsensing

Wireless sensor network is spatially distributed sensors with limited battery life and buffer capacity, in disaster prone sensor network event occurrence ratio is very high, in such situations there are lot of chances of congestion and data loss, reliable data collection in such situation is very challenging task, the proposed tour strategy with ADCN will help to reduce the data loss with tour strategy of mobile data collector with in the network with help of adaptive data collector node in highly sensitive wireless sensor network which will increase the overall reliability and lifetime of the network.

Mobile crowd sensing (MCS) has emerged as a promising sensing paradigm with the widespread adoption of smartphones. However, one of the key bottlenecks in MCS lies in the high sensing cost imposed on mobile users. To alleviate this burden, sparse sensing strategies are often employed, where data is collected from a limited number of locations and the remaining data is inferred by exploiting spatio-temporal correlations. Compared with vector-based inference approaches, matrix completion techniques can better capture two-dimensional correlations in the sensing data, thereby achieving higher recovery accuracy. Nevertheless, their performance degrades significantly when the actual sensing rate is low. In this paper, we propose a novel matrix-reshaping strategy that is applied prior to matrix completion to enhance recovery performance under sparse observations. We provide a theoretical analysis demonstrating that the reshaping process reduces the number of measurements required for successful matrix recovery. To validate our approach, we conduct extensive experiments using traditional matrix completion algorithms, deep learning models, and tensor completion methods on six real-world datasets. The results show that, to achieve the same level of recovery accuracy, our reshaped matrices consistently reduce the measurement overhead compared to their original ones.

Recently, we have proposed a novel fast adaptation method for the hybrid DNN-HMM models in speech recognition [1]. This method relies on learning an adaptation NN that is capable of transforming input speech features for a certain speaker into a more speaker independent space given a suitable speaker code. Speaker codes are learned for each speaker during adaptation. The whole multi-speaker training dataset is used to learn the adaptation NN weights. Our previous work has shown that this method is quite effective in adapting DNNs even when only a very small amount of adaptation data is available. However, the proposed method does not work well in the case of convolutional neural network (CNN). In this paper, we investigate the fast adaptation of CNN models. We first modify the speaker code based adaptation method to better suit to the CNN structure. Moreover, we investigate a new adaptation scheme using speaker specific adaptive nodes output weights. These weights scale different nodes outputs to optimize the model for new speakers. Experimental results on the TIMIT dataset demonstrates that both methods are quite effective in terms of adapting CNN based acoustic models and we can achieve even better performance by combining these two methods together.